{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('../py')\n",
    "import db\n",
    "import weighted\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original=pd.read_sql(sql=\"select * from _201904 where monthly_salary>0 and monthly_salary<80000 and YEAR(publish_date)=2019 and MONTH(publish_date)=4\", con=db.get_conn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91260, 94)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_original.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_job_ids=['104660258','104142922','108434795','101357291','106253516','110368302','111391233','108665401','109277048'\n",
    "                  ,'73857191','108584955','102824950','102824949','111391233','110884556']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data_original[~data_original.job_id.isin(error_job_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91259, 94)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "        {\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_2b72357e_6ef2_11e9_b036_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col5\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col6\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col7\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow0_col1\" class=\"data row0 col1\" >haskell</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow0_col2\" class=\"data row0 col2\" >25366</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow0_col3\" class=\"data row0 col3\" >21389</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow0_col4\" class=\"data row0 col4\" >7562</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow0_col5\" class=\"data row0 col5\" >45000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow0_col6\" class=\"data row0 col6\" >41</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow1_col1\" class=\"data row1 col1\" >julia</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow1_col2\" class=\"data row1 col2\" >21125</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow1_col3\" class=\"data row1 col3\" >21500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow1_col4\" class=\"data row1 col4\" >14000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow1_col5\" class=\"data row1 col5\" >37500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow1_col6\" class=\"data row1 col6\" >8</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow2_col1\" class=\"data row2 col1\" >rust</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow2_col2\" class=\"data row2 col2\" >18253</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow2_col3\" class=\"data row2 col3\" >17500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow2_col4\" class=\"data row2 col4\" >4917</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow2_col5\" class=\"data row2 col5\" >60750</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow2_col6\" class=\"data row2 col6\" >270</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow2_col7\" class=\"data row2 col7\" >0.07%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow3_col1\" class=\"data row3 col1\" >python</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow3_col2\" class=\"data row3 col2\" >17324</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow3_col3\" class=\"data row3 col3\" >15000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow3_col4\" class=\"data row3 col4\" >3750</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow3_col5\" class=\"data row3 col5\" >40000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow3_col6\" class=\"data row3 col6\" >29232</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow3_col7\" class=\"data row3 col7\" >7.12%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow4_col1\" class=\"data row4 col1\" >go</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow4_col2\" class=\"data row4 col2\" >17110</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow4_col4\" class=\"data row4 col4\" >5250</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow4_col5\" class=\"data row4 col5\" >37500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow4_col6\" class=\"data row4 col6\" >27522</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow4_col7\" class=\"data row4 col7\" >6.71%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow5_col1\" class=\"data row5 col1\" >matlab</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow5_col2\" class=\"data row5 col2\" >16920</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow5_col3\" class=\"data row5 col3\" >15000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow5_col4\" class=\"data row5 col4\" >5000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow5_col5\" class=\"data row5 col5\" >35257</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow5_col6\" class=\"data row5 col6\" >5786</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow5_col7\" class=\"data row5 col7\" >1.41%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow6_col1\" class=\"data row6 col1\" >perl</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow6_col2\" class=\"data row6 col2\" >16763</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow6_col3\" class=\"data row6 col3\" >14300</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow6_col4\" class=\"data row6 col4\" >4905</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow6_col5\" class=\"data row6 col5\" >40000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow6_col6\" class=\"data row6 col6\" >2511</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow6_col7\" class=\"data row6 col7\" >0.61%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow7_col1\" class=\"data row7 col1\" >lua</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow7_col2\" class=\"data row7 col2\" >16526</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow7_col3\" class=\"data row7 col3\" >15000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow7_col4\" class=\"data row7 col4\" >4000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow7_col5\" class=\"data row7 col5\" >37500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow7_col6\" class=\"data row7 col6\" >2884</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.70%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow8_col1\" class=\"data row8 col1\" >ruby</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow8_col2\" class=\"data row8 col2\" >16346</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow8_col3\" class=\"data row8 col3\" >16000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow8_col4\" class=\"data row8 col4\" >4930</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow8_col5\" class=\"data row8 col5\" >35000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow8_col6\" class=\"data row8 col6\" >1166</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow8_col7\" class=\"data row8 col7\" >0.28%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow9_col1\" class=\"data row9 col1\" >kotlin</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow9_col2\" class=\"data row9 col2\" >15249</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow9_col3\" class=\"data row9 col3\" >12500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow9_col4\" class=\"data row9 col4\" >5564</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow9_col5\" class=\"data row9 col5\" >32408</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow9_col6\" class=\"data row9 col6\" >831</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow9_col7\" class=\"data row9 col7\" >0.20%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow10_col1\" class=\"data row10 col1\" >cpp</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow10_col2\" class=\"data row10 col2\" >14985</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow10_col3\" class=\"data row10 col3\" >12500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow10_col4\" class=\"data row10 col4\" >3750</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow10_col5\" class=\"data row10 col5\" >37500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow10_col6\" class=\"data row10 col6\" >65830</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow10_col7\" class=\"data row10 col7\" >16.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow11_col1\" class=\"data row11 col1\" >swift</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow11_col2\" class=\"data row11 col2\" >14319</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow11_col3\" class=\"data row11 col3\" >12500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow11_col4\" class=\"data row11 col4\" >5250</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow11_col5\" class=\"data row11 col5\" >32271</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow11_col6\" class=\"data row11 col6\" >3151</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.77%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow12_col1\" class=\"data row12 col1\" >typescript</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow12_col2\" class=\"data row12 col2\" >13456</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow12_col3\" class=\"data row12 col3\" >12500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow12_col4\" class=\"data row12 col4\" >5481</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow12_col5\" class=\"data row12 col5\" >27500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow12_col6\" class=\"data row12 col6\" >1114</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.27%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow13_col1\" class=\"data row13 col1\" >php</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow13_col2\" class=\"data row13 col2\" >12894</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow13_col3\" class=\"data row13 col3\" >11500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow13_col4\" class=\"data row13 col4\" >3750</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow13_col5\" class=\"data row13 col5\" >35000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow13_col6\" class=\"data row13 col6\" >20137</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow13_col7\" class=\"data row13 col7\" >4.91%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow14_col1\" class=\"data row14 col1\" >objective_c</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow14_col2\" class=\"data row14 col2\" >12861</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow14_col3\" class=\"data row14 col3\" >12500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow14_col4\" class=\"data row14 col4\" >5250</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow14_col5\" class=\"data row14 col5\" >24143</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow14_col6\" class=\"data row14 col6\" >448</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow14_col7\" class=\"data row14 col7\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow15_col1\" class=\"data row15 col1\" >java</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow15_col2\" class=\"data row15 col2\" >12854</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow15_col3\" class=\"data row15 col3\" >12000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow15_col4\" class=\"data row15 col4\" >3750</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow15_col5\" class=\"data row15 col5\" >30000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow15_col6\" class=\"data row15 col6\" >137540</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow15_col7\" class=\"data row15 col7\" >33.52%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow16_col1\" class=\"data row16 col1\" >javascript</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow16_col2\" class=\"data row16 col2\" >11248</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow16_col3\" class=\"data row16 col3\" >10499</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow16_col4\" class=\"data row16 col4\" >3750</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow16_col5\" class=\"data row16 col5\" >22500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow16_col6\" class=\"data row16 col6\" >54612</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow16_col7\" class=\"data row16 col7\" >13.31%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow17_col1\" class=\"data row17 col1\" >c_sharp</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow17_col2\" class=\"data row17 col2\" >11042</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow17_col3\" class=\"data row17 col3\" >10000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow17_col4\" class=\"data row17 col4\" >3750</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow17_col5\" class=\"data row17 col5\" >26000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow17_col6\" class=\"data row17 col6\" >54959</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow17_col7\" class=\"data row17 col7\" >13.40%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow18_col1\" class=\"data row18 col1\" >vba</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow18_col2\" class=\"data row18 col2\" >10579</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow18_col3\" class=\"data row18 col3\" >10000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow18_col4\" class=\"data row18 col4\" >5000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow18_col5\" class=\"data row18 col5\" >22500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow18_col6\" class=\"data row18 col6\" >511</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow18_col7\" class=\"data row18 col7\" >0.12%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow19_col1\" class=\"data row19 col1\" >delphi</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow19_col2\" class=\"data row19 col2\" >10291</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow19_col3\" class=\"data row19 col3\" >9000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow19_col4\" class=\"data row19 col4\" >4038</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow19_col5\" class=\"data row19 col5\" >22746</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow19_col6\" class=\"data row19 col6\" >1661</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.40%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow20_col1\" class=\"data row20 col1\" >visual_basic</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow20_col2\" class=\"data row20 col2\" >9266</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow20_col3\" class=\"data row20 col3\" >9000</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow20_col4\" class=\"data row20 col4\" >4500</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow20_col5\" class=\"data row20 col5\" >17833</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow20_col6\" class=\"data row20 col6\" >76</td> \n",
       "        <td id=\"T_2b72357e_6ef2_11e9_b036_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.02%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11a845a64e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_53de7154_6ef8_11e9_802d_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow0_col1\" class=\"data row0 col1\" >java</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow0_col2\" class=\"data row0 col2\" >33.52%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow1_col1\" class=\"data row1 col1\" >cpp</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow1_col2\" class=\"data row1 col2\" >16.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow2_col1\" class=\"data row2 col1\" >c_sharp</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow2_col2\" class=\"data row2 col2\" >13.40%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow3_col1\" class=\"data row3 col1\" >javascript</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow3_col2\" class=\"data row3 col2\" >13.31%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow4_col1\" class=\"data row4 col1\" >python</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow4_col2\" class=\"data row4 col2\" >7.12%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow5_col1\" class=\"data row5 col1\" >go</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow5_col2\" class=\"data row5 col2\" >6.71%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow6_col1\" class=\"data row6 col1\" >php</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow6_col2\" class=\"data row6 col2\" >4.91%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow7_col1\" class=\"data row7 col1\" >matlab</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow7_col2\" class=\"data row7 col2\" >1.41%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow8_col1\" class=\"data row8 col1\" >swift</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow8_col2\" class=\"data row8 col2\" >0.77%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow9_col1\" class=\"data row9 col1\" >lua</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow9_col2\" class=\"data row9 col2\" >0.70%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow10_col1\" class=\"data row10 col1\" >perl</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow10_col2\" class=\"data row10 col2\" >0.61%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow11_col1\" class=\"data row11 col1\" >delphi</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow11_col2\" class=\"data row11 col2\" >0.40%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow12_col1\" class=\"data row12 col1\" >ruby</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow12_col2\" class=\"data row12 col2\" >0.28%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow13_col1\" class=\"data row13 col1\" >typescript</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow13_col2\" class=\"data row13 col2\" >0.27%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow14_col1\" class=\"data row14 col1\" >kotlin</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow14_col2\" class=\"data row14 col2\" >0.20%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow15_col1\" class=\"data row15 col1\" >vba</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow15_col2\" class=\"data row15 col2\" >0.12%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow16_col1\" class=\"data row16 col1\" >objective_c</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow16_col2\" class=\"data row16 col2\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow17_col1\" class=\"data row17 col1\" >rust</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow17_col2\" class=\"data row17 col2\" >0.07%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow18_col1\" class=\"data row18 col1\" >visual_basic</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow18_col2\" class=\"data row18 col2\" >0.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow19_col1\" class=\"data row19 col1\" >haskell</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow19_col2\" class=\"data row19 col2\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow20_col1\" class=\"data row20 col1\" >julia</td> \n",
       "        <td id=\"T_53de7154_6ef8_11e9_802d_701ce71031efrow20_col2\" class=\"data row20 col2\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11a97013dd8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=data_pl[['pl_','percentage']].sort_values(by='percentage', ascending=False)\n",
    "data_pl['rank']=range(1,22)\n",
    "data_pl=data_pl[['rank','pl_','percentage']]\n",
    "#data_pl.style.format({\"percentage\":\"{:.2%}\"})\n",
    "data_pl.style.hide_index().format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_pl=data_pl.sort_values(by='percentage')\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = data_pl['pl_']\n",
    "sizes = data_pl['percentage']\n",
    "#explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(15, 9))\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.title(\"Programming Languages in China, March 2019\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc=WordCloud()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in data_pl.iterrows():\n",
    "    wc_dict[row['pl_']]=row['percentage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc=wc.generate_from_frequencies(wc_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VWWZOv0pp4gReq9o55l0WSpZRwNKVDyhPn+oEy0+5Ntyc7hu/q/MoDzcV4JT7ShT6G0r2uCkZUZk7R6M4ue5DYqledOPmSSk34zsNKtOnu+7TDItEbhg7tbn3OHNDhzBj3bMnpYwoXvlwc1YHkRIZBp2h8E0ko5D5c28Q+m7X/UJoJHLDm3pO5eGiApzyUPCaZdq1ecKg+8XUimfQjLUrktyuKPQoTnzUdZ8QAjuRI9u/b8PH/zTdjJPqG0dfp9dZX7iE2+zeY72fNMvTuK1y95660Iil8lvfKOWBoQKc8tBwG0vrzlnTrfi6mYi3o5NxOLnuyihmPEOmfz+sM5x4p7IDB7Dj2e5tkZLFqz/uOvR3/5E0brIOpWHdfNqIotyMCnB3RXQW/F3+2ayLl3JvBG5z/eEwWF4abOMTmkPfoTTxEbLL/Smh6cQyvaQKoxs+/o2yQwcx15ncHbgtKjOnd8zixW6z0v09A4QmEF3W+lSU26GyKDdIN2zSuTEyhXFMO42UAU6zRLV0mVZ9fsdcXB4mYeAjo3V6NctJDQix4QSK0PcoTZ9i6MhLay7KQgisRJ5M/z4axYkNHf+aWLqXRG5o3XlkpamT6wby+1Hotbu/58tz6DRdJ9wRvVHlfqMC3AZYsSwHHv9lcj17lye2yuWvm4IoDFiY+oCJsy+ve79H2R4yCojuYk7YbZwBr12lus4CzJ01I3ejmzHCbhmXtykzcAArnunegiiiNPnRDhme7OgEr5t9qdiaZcKUB48KcHdICI3xw18hnRtj8sL3Kc5+gufW0TSdeKqP/pHH6R16BM+pM33xB9vdXGWLhV6b8vQn5IYOd53AbKcKpPv2UJnpnnF5K0LTyfTvxYx3L4/UrMzSqsyCWo1fUVSAu2NCI99/iLmrbzNx9uVVu9x2hXZjkXHNINszrgLcvSIEQujLBU8FoN2wJBl07o12/rtSjwyBGUtvWgme9UShT33hMn672nURZzuR7wS42TMbGgpNFsaIpfu61mGTUlKa/Ihwm6pkC6GBpnfmsAlt1bJwwKp/37its46m6qUpm08FuA2RtOrzXfdEoY/TKpPMDG5xmx5cnXUMLTTdQDMszHiaWLIHK57FjKUw7AS6GUM3LIRudh6nGWi6idANNG15m96pNn2v+W6T8vRpBg48t2afYcVJ5oaxE/kNpfFn+vdhJ7pnT0aBS2XmNFGwFcOTYvk9NdF0E920sZI57EQBK57BiKUwrPj1St/6crXvlc/HXHm+0PUtq1agPFxUgLtTUlJZukgyO4w+d4ow9FaGg4SmY8Yy2LEMjcrWVcF+EAmhoVtxDCtBIjdEum8vqcIo8Wz/uivn3y9Cv01l5jR9e5/u2mO0Uz2k+/bccYDTrQSp3l0Y6yxtVZ07h9eucy/rqHWCWQwrkSNZGCXdO06yMEIsWbi9quuKsoVUgLtDUkYsTn/I3uO/QOi7VEsXCbw2IIgl8hQGjhJP97Mw/SGJ9MDK8wLfwXO6z8FSbiA6Q4mJzACFXY+RGz6ybo/lfiWjkHZtgWZpqutSU3YyR6p3N8WJj+5o1ZZM3/i6S3PJKKI09TFhsPHklZvRdBMrkSPdu5vC2KOke8cx7Pv7i4ayPiEgUzBoVEPCoPOFSNM723Rd4LQimrUQITqzn3ZqUrgKcHdICI2RfS+iaRrDe59neO/zdL4xLw+xSEkUhRx84usIcb3uVXHuEy589K1tafNOoRs2ifwQAwdeoDD6yD2/X3Yv+V6T8sxpUr271wy/abpJIjNAIjtIs3ybPX2hke7bu25xVrdZorF0FRlu8jJnQsOKpckMHqB/32dJ9+y65wVclXvPsASf/Wqed18uU17o/M5ke02+9ncHcNoRFz9q8s4rVVI5nVTOYPbyzswI37lXkG0ikVQWz1NZPH9Hz2vVu69woXSYsRQ9ux5n5PiX1l1dvhsp5XJtuAAZRUh5/YflmmTICCklmm5i2sm7WpLrdoWeQ23hIoHXxLTXrsZhp3pI9e6+7QBnJ3Ik88MYVvf1MstTnyyPJGweoRkkcoP073uW3t1P3LLo6Y1kFC1Py1iu2Xfts0Eufy7XPx/djN205I9y+/L9JlZMoGmCajEgnddZnPIY3hujOOdhmBoykpi2xql36jSqIQiIxTWGx216hiy+8WtTtJshmYLBsc+mGdhl8dofl2hUQ9z2zsrOVQHuTsmIyfOvbncrHiiGnWTo8EsMHnrxptWe5fKFMfTaBL5D5LuEgYPvNPCdBoHXJgycldpxUXjjT0AiN8TA/mdJ5Ia24FXJTqHWuQv07n58zV4rniHVM8bS1durE5fu34OdLNAt2zAMPCpzZ+5qbt2nCU0n1TPG8JGfIDd85KbBR0pJFPqEXpvQdwgDj8BrdT4Tt0ngt5c/K2/l85BhQBj6yNAnP3KMgYOfQ9+CLx4Pui//l72YlkYUSd75boWv/M1+vvFrk/zKr47xnf+wQL7PZGHKJd9v8eLXCvzGr04wP+HSN2rx2IsZ+kYtnvlyliun2wgBT7yUIZHSaVQCTr3TYObSzurJqQC3YaKTIbbO8k4yCgn81tY3a4fRdJOBAy8wdOjz62Y4SimJAg+vVcFpFGkUJ2iWp2nXFvBa1du+jyU0fUtT6AO3SXXuLIXR42uGW4WmEc/0k8qPUp2/+WiAphuke3ZjrjO5u1GcoF1fuuvSQtcbJ4hnBxg89Pk1lbxvJKOIwGvhtSq0qnM0ihO0KrM49UV8t3Xbc/ES+WFgZ/UM7ldWTOPMuw1mr7rMT7g0awE9QxZTF9rsOZogntZ4+7sVzrzfZN8jnYW6oxAmzzl87w+K5AdMvv2NTmX7RFojlTVI5XT+6j8Wt/NlbZgKcBug6xap/Bip7Aimlex6T8JpFpm98tY2tG5nyQ4eYujw52+Yw7aajCLcVoXa3DmKEx9RX7qyY1bpiEKPZmkap7FEIrt22oid6iHZs4vqwsWbBoN4ZpB4dhC9S5ailBHl6VOE7uZ9mTLtznBxYfTYuo8JA492dZ7y9CeUpj7GqS1sXoBVNuy731zimS9nOfpsmjf/vMTClMtTL2U48UadJ1/KYpiCWjHATtx6usy1aZqduaY7kwpwd0yQ7z/MnmM/Sxh0hsi6LhQr1I34W9GtOMNHf6Izf61bTbMool1bYO78Gyxeenfz68RtAd+pU509SyI7wKd7+qaVIJkfxopn8VrldY+R6hvHThXWPX598fKm9UyF0EjkR+gdf3LdQqqh71BduMjc2deoL1zahMC2cy+g9xNNB8sWfPxmnePPpRndH2f6gsPP/50BXv+zMseeSeEbAsMSjOyLkcoZDO6O0W5EVBbXfmkMA4nvRhT6TYb3xigveLQbO+tLjApwd0hoGiN7X8B1qkye+x5Oq9j1D3yn9DK2U3bwIInsYPe0dynxnRqzZ77P4uX3NuV8QtO2PJEhcFvUFi7Rt/eZtRXDhegMUxZGKa0T4HQzRqowihXrvjRXdfYcfrvKZs19M+wk2cH92Otka0ZRSH3pCjOnXqWxdGVTzqnpOirI3T3T0jj8mRTxlIZhCs6+36BZDZm64FBd8rlwooXQwI5p7DueoFYOGdpj06oHVBZ93FbE5U+ujwR4rmR+0mXPsQQHHk9w+t2IdmN7VsnZKBXg7pgglupl4twrlBfObHdjdrTC6HHEOlMBZBRQmTnL0pUPNu18+vIqGltJyhCnvkSjOElu6OCa/XayQLIwSnnmdNceaiI3RCzV03UYPAp9KrPn8L1NHJ6Mpcn27193v9esUJw4QaN4ddPOqZtxFd42gduOePl3ltZs/8avdTJ13/zz61+iuj2uvOCv3H8DQML0RZdv/au5zW/sFlEFzDYg8J11h2+U2yN0g2R+dN3eW+i7LFx8e1Pv62iGvS21xbx2jercOWSXSga6aZPIDRJL93R9bjI/upw9uVa7Ok+rMruJc98EZixJLNPfda+UkmZ5itr8hQ2XFOrGsBLr3oNVlLuhrtJ3SkYUZ0+S693fWa7LsFHDK3fOimcxrPg6Q4YSr12jWZne1HMadhLDjN/6gZss9Ns0S5N4rUrX/fFMP8n8yJrtuhknkRvCjHWrai2pzJ7Bd+ub1k5NNzDj2a7JLLC8zmp9ad3XsVF2qvvqLIpyt9QQ5W3oG31iZfV2gUBGIZn8bsYPf5Va6Qq+31pT4NRz6moI8yaseHr9zEkpcepLm1o0VjMs7FQB/dP3wbaI2yxTW7hIX5dkESuRI5EbRjM+IbqhhmCnZ9fbdXjSd1vUFi9v6uRuoek3nWQfem28dm1Te9W6lSCe6e+sB6Uom0wFuNswtPtZDPPGdfciPKeOHc/TN5Lv+px6ZVIFuJsQmtEpn9KNlARec1PPF0v1EEv13HQi+b3kO3VqC5co7Hp0TSVuTTdJZAeIp3tplq/3WhP5YWKp7kOX9cUruPXi5tZ9u1bVfB1R6G96Ed90724Mc72e/MNLS8awxvoxB/L4M0XcK3NI/xZD0YZO6unDtE5cJGremzVJdxoV4G7D9MUfIrrU37oZ323co9Y8GKIw6CzbtJ5NvMeD0Ej17CKeGbj1Y++RKPRpVedoV+ZJ9e5asz+W6SeeHVwJcLoZJ5HtPjwpo5Dq3Dl8Z/OGJzsH7qyjulWEplMYe3RLlk67XwlTR0slIAwJa60bthsYuTTpFx7FvTiNN1e8ZYDTLIPCL72EN7mApwIcoALcbSnOfbLdTXjgBF5r/SAmxLoVqzciluohM3AAO9k99X2reK0K1YULJHvG1vRYrHiWeHYAzbCJApd4pn/d4UmnUaJZnt703pSMopsu96UZFobZfS3MjUj17iY7sP+Ovzw+SPRsivjhXfgLlVUBLqw0aPzoE8yhwpbUMHxQPby/WZvAsjPY8VynDpaMCPw2brtC4G/uorcPosBtIGWIlHLNxV4IjViqF023iMK7m3djWEkKY4+Q7hvf9kQG323SKE7gO3WsTy27pelGJ6gl87SqcyvTA7qpzp/f9EQP6EzNCNwmUkZd3yvDjGElsghNv+v7o3aywOCBzz0QiyzH9o+gpeIYPRn8hQrCMtHTcVofXSBqOtjjQ1i7BhCaIKy3cM5PEZTqmAN5Up89SuzgKP58GWusH3diHvf8zRfgNnqz2PtHMDJJkLLznMvLqfwywh4fxN43gmaZOBemcK/OQXTvagTez1SA2wChGfQOHSPbs49YooBu2EgZEfgtWvUFyovnqBYvbe4w2wMm8Nq4zQpmLM2ns1CFEFjxLOm+PVTnzm74HLoZIz96lN7dT647cXlLyWh5TtwEhdHja3bH0r3E0n04zTLx7EDXXmzoO9QXL+M7mz8ELmVEsLxw9acDMHR6cPHMALF0H+3qxudGmfEMgwdfIDt0EG0bpm1sKiGIHx3HGu3Dmy+TevYY7VNXMQcLEEY03j2Dnk2ipzrJTeZgD0ZPlsp3foTQNbS4hZaIIUwDYXeqm9+KFrfR00m0uI2wTDL7RijXXiesdn4nrF2DhJUGeiZB7tBzLP32y4SlTR7O3iFU3/eOCXoGjzJ28KdIZgZxWiVqpSs0KlOEgUeu7wCj+18ind+93Q29v0lJfenKuj0BzbAYOPBs1wvt7bDiWXrHn2LwwAvEs93ndW0Hr1WhvniZqMvcNTuRJ5buXRme1LoM3TXL07Sr8/ds2TLfa9KqzKy7P1kYIT9yFH2DQ5WJ3DDDh79A757PrEm22cnChkPtlfcxerK0T16kffoq1q6Bzn3NtkfUbBM22mhxi/jhXRBFeFOLND84j3t5jsbbp6h++0c4Z249gV76AbLtEDZaRE2H+MEx9GwS6FQn9SbmqL78LuU/exNzsIC9awB28HqSd0P14O6Q0DSG9zxP4DaYOPdXtOrzBIGHEALTTpIt7GFw92fpG36UeunKdjf3vlaZOUXf3qc7GZWfHqbUdDL9Bxg8/AUWLvwIp764zlFW060EqcIYueEj5EeOYCcLK8e+tmbodg6Jhb5DqzzTdQFm3bSwkwXSfXu6Dk9KKanOXcBr3bvK8IHTpLZ4mezgwa7DlGYsQ8+uxwm9NqWpj2870cVKFsj07aEwepzM4AF0w77hc4kAsWOHKqWEsNpAhiHScQnKdYy+fCdRpDdL+oXjnSxIx0Nm2C7jAAAgAElEQVR6AcLQO1NkNjLCY+gknzqElowRLFaIHBc0seo+nTdbQgYB0g8Iqw30XGp5Ss7DN6KkAtwdEyRS/Uyef5XK0oVVe8KgUwsrlRslnux+/0S5rlGcpLF4hdzQ4TVz4oQQ6KZN/95nsBNZqvMXaZVn8FplAq/d6fkJgWbYy/eGcsTSvSRyQ6R6d5PIDKwq0CllRH3hMmgayfzIupOZt4LTWKK+eLlLhQGBnerBjGewEtk1z/NaVZqlyXtahinw29QXL9OuLpDIra2AIIQgkR1k8NCLxLMD1Bev0K7N47VrnQQVKRGajmbYmHYCO1kglukjmR8l3bsbK5lfNVVDRhGLl98lO3gAK5HfoUFOrpqtIZfvdwlNw+zJYu8aYOm3XwHAHMhjcsPUoigCAZp9e0O1mm0SP7SL5o/PUX/9JPaeQWSwehTEGuvDm5xH2CZ6Pk1Qqj+0t0tUgLtjEteprZviLuj80fru5s7jehBFgcfcuTdIFcYwuiQbCCEwrDiF0UdJ9Y7j1JfwnTqh7yCjCCEEQjfRDRszlsJK5rBi6TX3daSUNIqTzJ57DTuZx4pn0ddZnX8reO0ajeIEPbse6yxTdYNEfgiBQNfXBuD60mWcRvHeXqykpF2dpzjxEbFUoZNA9SnXatlZiRyZgQO4zRKB2yQKvE7SkKaj6QaGFceMZbCTeQwrsSYbUEpJceJDZk6/itB0enY/8WBV4YgkQbVJUGmQ/9nniFwPI58mal/Pfg3KdcJak9Rnj2LtGsA5M0H71BXs8UFih3YRPzK+Uq6m9fElvOkl3Ml57P2jGD1ZZBh1qqeHnSAX1ltYw73kfu559HQCb2IBb2JeJZkot0fKiLmJdyj0H6JRmaJZm10uVSKwYilyvfuxE3kWJt/d7qbuCLXFi8ye/SGjx78M+tqhSuhcUO1EbkOJIlJGNIqTzJ19jdr8eRK5Efx2jdg2BjgZhbSr8zTLM2QHVi9svF7VgCj0qS9cwmvfu+HJawK3RWnqJPFMPz27Hls3TV03LBLZgeVSQHdGyojixEfMnP4+TqNIbeEiPWOPdWq+7CRS0j55CQlI16P8J68TtRzcSzMEpRr+XJHKd36EUcgQtRzap68idG0l4ASVBs13z+DvGkAICJudDOyo7RIUq9TfOAkCpOMTNR2k41F//STWrgE0U8dfquJNLeDPlZCeT/lPXiNquRiFNIFp4l6ZJaw9vF+2VYC7QwJBLFEglRtl/MhP47bLy6VxBIYZJ5bswTDjhMEj5PsPrzyvXplifuKd7Wv4fUqGAQsX38awkwwceH5Thw6jMKC+dJn5c29SnT9P6Ls49cVOkJByWxf4depL1JeukOnfe1vTF9q1eVrVOaItqUguceqLzJ1/A82wyA0d2tQqDGHgsnTlx8yff5NWda6TcLR4uTPsLI0dt/Cye+V6Rmnj7dMA+PMl/PkSAO2PL6//5CDsTMyeXFi12Z8v4893L6HkzxbxZ7tX2G59eKHr9oeVCnB3TBCL52jV5hCajh1f3asIAofAb2HHs8s3kjvbPae2DW3dGQKvxdzZ1/DaNQYPfm5Teldeu05l5hSLV96nWZpaCQy+28BtlggDb9U9uq0WeG1a5WncZuW2Xm9t4TJuo7QFLeuQUUizPMXMqe/ht2sUxh5ZZ9HnO9OuLrB45T1KkydwGqWVpcbcZhm3VSaeHVRLlyubRgW4OyRlxNTFH9zx8zazZteDyGtXWbj4Nk59kdzwYfJDR9atYr2eTpHUBvXFS1Rmz1BbuITbLK9er1FK2rVFfLexrQEOJO3qAs3ixC0DnO80ViaIbyUZBjRKk/hOnUZ5kvzQEdJ9e+440EkZ4dSWqM6fozJ7lsbS1c5KNjc+JgqpL13tLKe2w3pwyv1LBbg7JqmXJ7e7EQ+kKHCpzJyhVZmlPHWKRH6YRGaAWLYPK5ZBM2103UJoGjIKiQKfwGvhO3WcRol2bZ52dR6nvojbLK9bVb06dxavXV0zl6tRnNzcxYtvwW2WmDn9KsWpkzd9XBR4NEtTm7qK/22TEW6zRPHKhzSWrpLIDpLIDhHPDmCnezDtJJphoy8n9kRRSBS4BG4Tt13DqS/Rrs7Rri3i1Bfw2vV13+O5c69TnTu3KsDJMNiiYVnlQaQCnHKfkXitCl6rQqN4tVPDzU6iGxZCM9A0DRCdLNYoJAoDwsAl9Bx8r7mSqn4zXqt6T+eS3a4o9GmWp1dVELhfRaHX+fJQW6I6fxHTTqJbcTTDRBP6ypqZUkadLx+hT+i7BF6rk2G5zpeNG7Wrc3e1QoqifJoKcNske6AHM2Gx9NHsqu2JgRSpsSyNqSqtudXLMRWODxC0fBqTVSK/kxas2waxnjit+SYy3IZv+PdQ6DuEvoPb6H5DXdl6UoYEboNAVctQdgAV4LaQ0DWy+wrkDvRi5WycpRY5p4/cgR6aszUqZ5dAgJ2L45TapHdlSY3lsPNxapdKpEYy2Pk4/U+NUD67RPXCEv2fGUFoAqfUJnzAApyiKMrdUAFuC8X7kiSHMzSmqySjNKmxLJqpIzRB7kAvRFCfrK6shBDvT6HbOtWLxU5QzNi0iy0aExV6HhmkNVujtdCgcKQfTdfYukpeiqIo9z+12PIWMhImuqXTnK7hltuYKRsjbuBWHWqXy7QWVg/7CEMjaAc0pmvoMQPdNmgvNqldLmPEDIQu8KpuZ6WDe5V4JuXNC5MqiqLcp1QPbgu5pRYyKrDrqwcRmqAxVSVoeuT2FahPVDHiJtl9PfQ+NoiRMImCkMRAmuz+Hry6i1dzkUG00sOLFRJk9/eQP9JPc7rOwvtTRP6dD1MKTUNbZ2X3a0kDiqIoO40KcFvIq7nMvzOJkTCRoST0OgU/zbhJ6AYETkB7oUHl/BKhE1A41k+tVaI+UcWtLC/h44aEbsDlPz1D0PJoztVZeH8ar+YSBRu7Byc0A8OMd90nI5WmrSjKzqQC3BaSkcSrdXpiN/Jv+P/QCVb2O8UWMohozdYJ3dX1v5xiZ6Js0A5w2XgFcU03O7XH1lki61qqt6Ioyk6jAtx9rHqx1CmYuMGe2e0wrASZvr3rlikJ3BZuq3LPzq8oinKvqAB3H4u8e3vvSwiNeHaAwujxdR/jOXWcupqHpijKzqOyKB9agnimn9HjX0a3ut9/C7w2Tm0ev60WilYUZedRPbiHkdBIFUbZ9fjPkeoZW3d40qkvUp2/yMNY6l5RlJ1PBbgdJJEbpm/v0wS+Q2PpCq3KDL7TuIMKzwI7VaB/3zP0jT+NGUuurCH4aaHvUl+6Sn3xJrWsFEVR7mMqwO0gmmGR6dtLPDvQmZ8mIwK3tVzEs4bfruE5DaLA7aT3hwGaYaKbcWLJAqmeMeLZQTTdRGj6uj03pKRRmmTx0jvIKOj+GEVRlPucCnA7jRCrqivrho2dzIGks+LIOr05IcRyGRKxfmBb1qrNM3fuDVqV2Zs+bqfYtVvn7/0PKXaP6/xf/7TBG6+reX2K8jBQAW6HE8tBi7tcrUsuB0anvsj0J69QvkWNsvtFLC5IpwXVSoS3TtzSNLAsQSwmWGdEVlGUB5DKonzoSaSUyCigvniZS+/8AcWrH253o26LpsFLP2Hzz/9llgMHze1ujqIo9xnVg9tROkOQ13pbtxpqvOmRpAQkMgrxnTqLl95l7twbO2rVkmxOcOiwgWXdq5WmFUXZyVSA20H8do3q/Hk03cCMpTsZkEIghNb578o4ZbcL/nJglBFSSqLQx2kUKU18RHHiI7xNWK1E1yEeF4QheJ7ENDtDg0JAFIHryjXDiOl0Z9iwXpNEXRZs0TRIpQUygkajE9ivHXfXLoNHHjUxDEhnBLnc9dftuBKnywpmmgDb7jxf0zoTIAK/07awy7x6sfx489rjJQQBOO217bUsiMUE7eV9sZjAWP4Lu/aerDeMqijK5hPytlPM72EjhNj+RuwonXT/eKafWLoXO1nAiqUxYyl0K4FumKAZaJqOjCKiMCD0m3jtOk59iVZ1jsbSVZz64qa26ugxg1/93zKcPRPw8ncdvviTNi990SaX05ibi/jDP2jzx99qUyxGSNkJiH/4pz0cPmLw818tcu7s6oxNIWDffp3f+YMeZqZD/vbfLBMGkq/8TIyv/bUYe/cb9PR0Rtl9H6Lo+q/Rf/hGi3/26w1kBON7dP7H/znN+B6db/5Wi/5+ja/8dIzhYR3HlXzwvs9v/rsmH/7YXxWAdB327TP4L/6rBC98zqK3T6Pdlnx8wuc3/k2TDz+4/njLhp//hTh//39J8ev/uE61Kvlbv5Lg8JFOD/Pq1ZD/9Mdt/vD321Qq6tddUW6HlPKuhmdUD25HkriNIm7j/lxC6/EnTT77nInjwI8/8AF49HGT/+kfpOjtFfzrf9WkUun0mP7wD9r8g3+U5pf+epx/8mv1VUmgtg3PPmdjmvDWmy7lUkQyJVhaDHnthx5nzwZ88SdthIDvveIyP3+9S3XiQx/5qR5WJqPxd/5uEinh3JmAkyd8DhwwePFFi337DP7h36/y4Y/9lTY89bTJr/2TLIWCxskTPm++4ZHPC558yuI3vpHnV/9Rjf/0x86qnlwiKfjFr8fZvdtgZibkle+69PZpPPKoyX/736XI5zX+xT9vdO0tKoqyyeTyPZ3t/KEzUqR+dvjP0WOG/N0/KMjzVwfk//F/ZuTYLn1l38HDhvxP3+mRJ88OyKc/a0pd72zv6dXkW+/1ydff7pPptFh1vJ5eTf7mN/PyB2/2yqefMbue73d+Py9/91sFeez42v3Xfsb36PL//n9z8sLEoPy9PyrIZ5+3pKZ19uVyQv6L/ycrT10YkP/1f5NYaUMmI+TvfqsgPzzVL3/x63Fp2dePd+SoIV97q0+++2G/3LvPkIC0bOQvfT0uL0wMyvdP9Mv//u+lpGF0Hm9ZyJ/5uZh8/0S//Obv5eW+/fptv6fqR/08zD93G1tUFqWy6Rp1yZ/9icPkxPVuyrkzAd97xSUIJJ//gk0s3hl5KC5FvPJyp5fz0hevF13VNBga1njiKZMrl8OVnuDd8H3Jd/7M4YP3vJVeV6Uiefstj6XFiF27dBKJTrteeNFi126dt97w+P5fOXg3VDg6fSrgm7/VIp0RfP0/j606h5SSyYmQb/z7JsHyiKvnwflzPh/+2COT0RgeUXMVFGUrqACnbLrp6ZBaTa7ZfvoTH6ct2X/QwLohq/9bv9/GcyW/+PU4+vK137YFX/zJGL4HL/+luxIs7sbCfMTMTLgm0aNSkXieJB7XVubJHT1qEo8LfvyBh+OuPdZbb7poGjz51Oo6ep4HFy4E1OurX7/rds5j6IKYrbI+FWUrqACnbLpWS+IHawNcrda575ZJa2ja9Yv8hx/4nDkd8JmnLfbu69wWTqUEP/klm2Ix4tXvOZvSrmYzwmmvbVcUyTULwGRzGroO5VJEFK59ztJShBDQ27v6TygMJJVK9/p9UnJ3s/EVRbkjKsApm840O0OMn2aYnczIIFgbUP7j77TRdfjFX+704vYf0Nm3z+D99zympzan4GsUQbQ2VnXlL7fRNLv/iZhmJ1L5/uoDSiBSCSSKcl9QAU7ZdPm8Rjy+tqsyPKxjmrCwEBF+qlf0yssOxWLIl75qky9ofOkrMZqtiL/48/V7b1J2fgQgNvk3eXqqM5Q5OqatBLMb7d2rE0Vw9YqKZopyv1IBTtl0A4M6Bw4YJJPXA0MyKXjqaZNYTHDiIx/XWx3galXJt//UYWhQ58XP27z4BZupyZC3f7R+ckkQQKsNdkyQSW/u2N9773iUSxGffc5iaFhf1SNNJgVf/ukYvg8/+EGXG3SKotwX1Dw4ZdP5vuQXvhan1ZacOxsgo848uOc/ZzMzE/HWGx7upzpmUsKf/LHDX/8bCb72SzEGBjX+6FttHGf9McV6LeLK5YCjR2O8+JJFtRYR+KDpUC5GzM1tfGjz45MBP3jV5a/9cpy/8TfjfOfPHKpViWHA40+YfPWnY3x80ucvv60CnKLcr1SAUzbdlUshrZbkb/3tJL4v0Q3B4JBGuRTxb/91k6mpoGtVn/NnA378vs8LL1osLER89y9unlyytBTxg1ddnnzS4ud+Ps5zz9s0GhFI+PM/dfidb3ZZq+s2RRH82/+viW0LXvy8zWeftSiXJYm4oK9f4+TJgH/xz+oUi5tzf1BRlM2nApyy6RoNyW/8myaDQzpPPmWSSgt+/H7Eq99zefdtD3edTk8UwWs/dHn+cxYnT/hcOH/z+1tB0BlK/PV/XOMLP2EzOKSj653AN3HDHLxWU3LiI5/5uZDi0tqAtDAf8ebrLhcvhqt6jAvzEf/0n9T54k/ZPPmUSU+vzkxb8p1vB/zFtx0mrl4/RxTCzEzIq3/lrllyDDqZpR+f9KlVIxYWVFBUlK2g1qJ82AgwcimSR0dXNoUNh9bZaSLn7iZTX1uLUmjwv/9qjU8+vrPJa4YB/+h/TfOLX4/zD/9+je/cJMFEUZQHn1qLUrkjQtdIHBhi/B/+8sq21oVZrv76t/Bm776iwN3Yd8DgS1+NceliwBuvqXtbm0GzY+ipNHoiiWbZaKYJmo7QBEQSGUXIMCDyPCLPJXJahK0W0advkm4WITBSaYx0Fi2eQLMshKZ3llYKAyLXIWw0CGploq0qvSAEeiKJkc6uvE9C1wGJDEIizyFoNglqFaJuJSqU+5YKcMq2evwJE8OEVErja78YIx4XfPO32l1XQlFuk6ZhFXqxevqw+gex+gax8j0YqTRaPIEwzM4FPAyJggDpuYStJkGzjl8p45eW8MtF/GoZv1IibDZufj4hiA2PYfX2r2wKGnWcmUmi9vX6gma+gD04SnxsHHtoBDNXwEikEKbZqXrhuYT1Gu7iPM70VZzpSdy5aSJvA192hMDqHSA2fMNIRbuFOztNUK+ubDMyWezBEeKju7GHRrEKvejJdOeLAJLI8wgadbziIu70BO2ZyTWvS7l/qQCnbKu/9XcSDA/pZLIalgXf+r023/5TNTS5UWahl/iuPST3HSIxvh89lV6/MK5hoBsGxGKdC/3yZiklUbuFMztF7eQH1D56j65ZQcuErpN5/BnyTz+/ss2ZnWL+23+IM3kFoRvERneTOf44yUPHMVJpxKdWAhCahmYYGIkkdv8g6aOP4sxOUT/xAY2zH+NXSnf2RmgayQNH6P/yz69scpcWWPret2mcOgG6TmxgmPSxx0gdfgQzX+jUV/wUPW6gxxPYfQOkDx3DXZyn/slH1E9/hLcwT2dqv3K/UgFO2TSlUsTL33UQy/++HR9+4LM0FuF7kksXQ/7yLxzaXZbTUm5B04nv2kPmkSdJHXkEPZ7YcMV3sTxkZw8MY1y5cNPgth4zm++0wTBJ7j9E/tkvEBvdhWaYt36yEIjlXqHd24/Z00vlndfxlhY28GquM1IZjFQGYRjEx/aQ/+yLJPYcQLPtWz95uV12/2CnZ9zXT/nN7+PMTN5Vm5R7SwU4ZdPMzUb8u39zZ0M3v/nv1VDP3RK6QfLgEfLPfYH4yC6Efos/62sB62YBUEr8SonW5QsbapMeT2BmciT3H6bwwk8QGx5bvq/VEfk+0veIggCh6517cYa5KigLIRB2jOwTzyAMk+IPvktQLW+oPQC6bWNmssR37yP/zIsk9h1YFXCjIED6HjLwQWidNpnWmi8KQtfJHH8CzbRYePlP8e8y8Cr3jgpwirKTCUHywGF6Pv8l7P6hVUHkmsjzOvfVamXCZqMTXMKgcxE3DLRYAiOZxMjkOskflkUU+LgLs7jzsxtuV2L/IYxUBntgGKHryDDEXZzDW1zArxSJ2m0i30MYBpodxyr0YA+OYPUNrAoqmmmRPvYYQb1K6YcvIzdaLVYIYqPj2ENjxEd3oxkmMgrxSkt4C3N45RJRq9lpk6ah2bHOfcOBYey+QYSx+nKZOnSMoFFj4Tt/hNyMchfKplMBTlF2sNjILvLPvYTdP7gmuEWBjzM1QevKBdy5GbzSImG9RuR7nQuyEGimhZZIYqQyWPkCZk8fVt8AmmHSunQe6W88kzG57zBC1xCaTtBs0Dx/isaZT3BmpwhqVT5dct3I5IiP7ib9yBOkDh1fdZ9Os2wyjzxJ++pFWpfOb7hNsdHdCNHp9YbtNq3L52ic+Zj21FWCanlN8NRTaWLDY6SPPErqyCNodmxV8E0ff5Lm+dM0zny84TYp944KcIqyQ2nxBLmnniM2NLJmWDJ02tROvE/9k49oT15mpcLrjaTsTA3wXIJKCWfqCug6ZjaPkUzjlZfurn2meb0tH71H5d3X8Svlde/pBbUK9VMV3MV5QJA+8sjKPiEERjpL7qnnOlmMzsYSkbTlXljkuTTOnKT89g9xF+a6vz9A2KjTPHcKb3EeGQZkHv0MwrpeA1CzLArPv0Tr6iWVWXkfUostK8oOldx/mMSe/Wjm6qKrkedReed1Sq+9QvvqxXUv3l2FIX5pifbkZcJGfVPa2bp0nuoHb+OXS7eVsOItzlF67WXcT93b0gyD2MguYiO777pNztQE1R+/3RmCvY33xy8XKb31A5yZyVW9PAHYw2Mk9uy/6zYpm08FOEXZgTQ7RvrII+ip9JpkkfrpE5TfeZ2gXtum1l0XtprUT32IXyne0fOcuRmqH/xodUAUAj2eIHXw6N21yWnTOH8Kd27mjjJE/eIi1Q9+ROR7qxJ1hKaTOf7kXbVJuTd29hClJjAyCezhAuZADjObQIuZCKszNCKDkKjtEtTbBMUG3nwFb6mG9HbADWFdw+rLYI/0YPZlMNJxNNsEQ4cwQvoBYcMhqLXxF6u4s2WCamtDKd3A9ek8AvRUnNjuPuyRns55YxYIiByfoNrEnS3jTix2zrdBwtSxhwtYAznMnjT6tdena0g/IPICwmoLv1jHnSriLVQ2NOUotmeA9JN7MTIJZBBS/dFZ2udXJ04YhTTx8T6swTxGNtH5/ZGSqO0R1Fp4C1Xc6SL+Yq37+ysE9lgPhZ98DICw6VB96wzuZHFlv5GJE9szgD1S6LxW00D6AUG9jTdbxrm6iF9q3PbnZw+PYvUNrkm798pFKu++cevJ2VvEmZ3CXZi78ySMKKJx+iSF576Akc6ubBamRWxkF3o6Q7jBAO4tLWx4Annj/GnylRJa/9D1e3FCEB8bx8jmCKrbuxqQstqODHBC17AGc6Qe20Pi0AhWfxazkEZPxxCWgWZ2XpYMQiLXJ2y5nYtlqYE7V6b21lla52eR/n0Y6DRBbHc/6Sf3kjgwhDWQw8gl0ZMxhGkgdA0ZRRCEhG2vE+QqTfylGs50kfa5GRonryL9O8g0kxLph2hxi+SxXWQ/e5DYrl7Mvix60u5c8AVILyCst/GLdZyrC9Teu0Dj4wmi5u1fKOzhAokjo8T3DGANFzB70p0vJkm787lpAhksB/CmS1Bt4s1VaJ2fofbOedypO+sJ2KM9FL78OLGRHqQf4i/VVgKclrRJP7GX9JP7iO/uw+zNoKc67zNSdn53mg5+qYG/UGXut3+IO7W0NtBqAnsoT/8vPQfQefxSDXeyiBa3SD26m+yzh7B39WH1ZtCSNsLQO59h0115P+sfXKJ+4grhbXxxSOzeh55MrtneOPsx3uL8xr/obDJnemLDwTZo1GlPXCZ97PGVbULTOnP0+gZpbTDAucsZkxsRtVu0r17C6h1YSYIRQqDZMWJDYzRUgLuv7LgApyVs0k/sIf+F48T3D2H2rl0V4Rqha2i2iZFJwGAe6Hy7dq4s0Do/s5XNvi3CMkg/uZf8Fx8leXSs05voMldJaDoYOlrMwsynYKwX6Cya3Dw1QfP01B0FOBlKhG2Sf/YQhZ96lNieATRjbbo5ho6esLEGcsT3DxE/MPz/t/eeQXad553n7z3pnps7Z3QjZ4AAARAEcxBFSSRFySMreez1euVZe7xxPOP1fnBt1c7ObtXMTm1tWTu2Z8aWHMailSxSEilmEswEkXPoBrqBzunmcNK7H06jgUZ3Ax0QuhvnVwUU6uLec95zw/s/7/M+z/9Bf/0Q6fdPzmg1l3xwAxUPbSC8qgG9Luln2E11fYYChoYaNTHqkoRXNRLd0kZ4ZQPDrxwgf7xrxtc24biaihI2ELqKGg9T+fgWKh/bTKilevym6Kpno0ZC/vXWJnHbaun7+70zO4+uYjRUjp1jM5VPbCW8oh6hXvM9vfwZVscxV9QRWduM0VTJ6NvHsAfSUx8cQFUJ1TeihsITHpaOQ/7UcT+EthCQEmt4EHeO/o3Scyl0tk8QOPATO4yaOgodZ+Y0Jic9ipuf+/5iobOd5Pb7mDB9KgqhhiZyp47O+bgBN59FJXBqIkzlo5upemobZlvtpAlDuh5e2cYr2yAlimn4IcurBNDqT2MNpGe3wrkdCIhtaaP2+d1E1jdfWYVKiZst4qTyeEULKSWKoaEmIugVUf89GBMJNWbiZIr+Cm8WKCHNP/dXdhNq9G8EPMvBGc3hZIvgeaixMHpVzA9XAoqhEV7VQE1oF26+TPqjU8jy9VfEaswktqUNrWLiysOzHOzhLG6+5K8kDQ2tMuqLtxAIRaBXREnuWYfQVezRHFbPHO7ABahRE702ScVDG6j+wnb0mqRvPCwlbsnGK5bxxc1AhK4UHhc7+nFGczMKkyqGRnhFHcn711L7/G6M+gqk5+FkC9jDOaRlI3QNrTKGloggFIGia5httVR/cQdCURh+7RDO8NSTsO/IEZ9UFmCnRrBGh2eXVHILcUtFf/U217o1z/MzHK9B6AZGdd0UL5jBIa0ybj6LtOfeOaM80DvpNyaEIFTXOOdjBtwaFo3AKWGDxM7V1Hz5vvFJGMCzHcqXhsmfuEj50jBOuoC0HSSg6BpaMoLRUIm5vJbw8npyxzqxeucWnriV6NVxEvevJbK2aVzcihcGyOw7S+n8AE626IdUpRxfiWjxMKHmKsy2Ov91EZPRd47NWryNuiQ1zzyOuTMAACAASURBVO0k1FiJkyuSP9pJ9tB57MHMFVE1dYy6CuI7VhHb0oYaCfk/6uZqKh5YT+nCAKUL13d0yB5op/rp7ajxME46T/FsL4X2Pqy+Uf/6yjbS9RCqihozMVtrSNy/jshqf+JQQjqxza1UPLSBgR9+MKf3WauIUvXEFioe3ohem8QeyZE/3kXxXC/2SA5Z9ic+EdLQq+KEltUSXd9M9kA73gz3boWuEtmwjFBTNXp1nFLXIOmPTlO60I+TLSEdF6EqaIkIkbVNJO5bQ6ipGgQYNQkqn9iK1Z8m9d7xKT9LPVmB0CfbS5UHen0XjgWCWyjMryOAlNjpUTzXQbmqDEJRNbRkxdzGVCrhTteQcIY46TTSsZHyqpo4RUGvrJ7XcW8niiZouKeWkXMpzAoTgFDCoDBUxIjpJFri9B4Y8G+86yOMtKeJ1UdQdIVyukyyNY5reWR6cpTTCyRiMAWLQ+AUgdlWS81VKwzw9zrSH54k/dFpyj0jvrhdMwkJXfVXO1UxjLoKP1lgNH+7r+CGhJbVEF5R7ydaAOXeUYZf3k/6g5M4meLUeyqKn7ygVcUJ1SXR6yv80Ks3u/0XNWoSjprYw1lG3jhMau9xyr2jk99LQ6Nwphs3u4uKhzagmAZCEUQ3tWIur6PUNXjdc9uDGUZeP4S2L0rx/ABW/yj2YAY3N3VNU+6wSbGjn7pvPER0XbM/1liY2JY2hl/Zj5udfS1UbGsbQlXRKqPkDl9g9O2jFE53Yw9lxlb+V56rmDpapf+9sfpHZ3zjIBQFvSKKFjMpnOtl4EcfUjh1aXIYVwhyx7soXRyi+ks7xoXcqE+SfGAdpQsDFDsmr2CuuN1PxB4ZnrvLxy3As8pIb37jkZaFLJcgErvyoKqimubcjnfZims+Y7ItvFIJNRqf8LgaiczruLcToQiqV1cSb4jSc3CA2g3VpLsyVK+tJNOdxUyGcCyXSJVJrCHK8NkUWlgjUmWiaAKz0qScLhNviAYCN1+0eJjknvWEl9ePP2aPZBl5/TDDrx687n6FtF2c4SzOcJZie58vFAtj/30CekUULXnlB1K6OER+qknxajyJkyrgpAqUOvpRwgZecW5fNjdfIvPpWYZ+uR9nZOrQmLQciu39pPYeJ9RcTXSD34pETYQJNVejRk3c7PX3W1Lvn/TPlyvdMBHCzZfIHjqPGg8TWdOIUBR/5VMZI9RUTeF096yv06ivBAG5g+cZ/Nkn5I53ja/arsUr2Vi9o1i9c/M/tEdyDP9yP9n955DO1IXWzkiO1AcnUWOmn3BTGUMoCtGNywivaaTYOQDuxNcqpjmlJZdbzM86PH0r8Wxr/oIr/ZY16jXaIVTN7yM3SwH1HOem2GpNlYE51zHdKaSUlNJlEs0x1JBCuMoEAVbWRihgxHRi9RHijVGidWEi1SaJ5hhSSuy8jV100MyFLSELe3QAAvTaJMkHN/h7JYBXtskd62L4Vwf81O2ZMsuVze1EIvDLRn2UkDZ1osd1mKu4If29yeHXD00rbleeKymc7aF4vp/I+mbfEFcIjLokauzGAnej/590OsuhcKYHeziHUZsA/D0uvSp2g1dOjVAEVn+K0XeOXlfc5ot0XEoXh8h8enZqcbsKL18me7CD6MZlJO9fB4AaD/tJVAc7Jt3AKZoOYnJilVcuL5jsSQA8d97jkWMCdzVirPZM6DqyPEsx8TzkTXiPPGusFm4sROmPSUEJhXAXgaOJ53h0vt9NOWNhRHVSXVmEANfysAs23fv6sfM2qa4MhZES5azNSEeabG8eK28jXYnneAh1Xg23bzkLXuCEoWO21WLUJcYfs4cyZD45MztxW+A4qTxOpjAegg2vbCCxazXWYMZPbriFeGWLYnsfpY7+GT3fzZX8kF7JRg37SSdq1BwPr9788dk4o1cETmgKSmSGLU6mIHdsLNP0FokbgFu0KJzuxs3PLIxa7h6h2N5H/N6VKIaf3GK2+Ht4kyIUirj6XmgceRMEZWEyVe0h02ZP34ibMiVP9z4rC3vCv4z0INvjb9VYucm/g/RF/0bXLjgUhvzvsJ1fOPu7M2XBO5moYYPI6sYrX2YJ1mCG3NHOOzuwm0yp00/SkGPhKC1mUvXUNpr/2eepeHTThPDlzcbNlym29yGdGd4NS38C964SCCWk+bVdtwIp8a7e/xJizueSrkexvRd76NbeHHlF64ZJN1cjLQerP42TuqoDdl1yUsapf/Cpw+xC06/fAuc2I4RyU8YzVfsfKSXeXPbSFHFzxqRNM6Z5ZGcG3HwW/ApOCemExuq8ADzLxhpI4yzARJH54KTypD84idlWS3RdCwiBXpMgcd8awmsasT53D/kTF8ke7KDY0X9T3Vi8kkV5tmn3rjcx5DuXiUNTMJuqMRorMRqSqPEIajjkp+jrql/YrquoEZNwW+3sjj0NTmYsVf8Wl4lI28GapYg66TxOKo9R5zt36JUx1OjkZArp2JOc+MF33BdCLJgtZqFpc15ljR9jrOPB1Ugpka47p1T/y/tk80XoxoTvu5TSD38GAregWPACJzQ/nfoyXtnGHskuvVCMJ8mfuEj/C+9T++X7iG5uRTE0X+AbKjFqk4RXNlDx8EbK3SNkD3aQ+eycvxKZ596iZ7s4mbnsG1x93pmLW6ilmsTutcS2tKFXxVFM3a9X1FS/+FtVxwVTKAIUMefu1Nfi5kt45Vuf9SVdb9rs0OnwitaEfVShqyghzZ9Ir/q+u8XilIkSajQG8xSUm4lihOYvJkJBCU8saPedd+b2GSq6PuXqa7ao4ckRFc9aYHugAQtf4FAU1Kv2W6Tj4RXmV8eyUPFKNrkjF7D6RonvWEXl41sw22pRxiy6tGTEr+trrCSyromqz28jd+g8o+8cpdQ1NHeh8zy80q2f9BVTp+aZnSQf3OCH3+ImKFO7mdwqpOXeMOnjppzHk7NeZXu2g3eVfZwQwt+P05QJK043n50yPGdU1964m/dtRIlEfduzOSP8gvZr/Dal6+DM0f5LCZkoxtz3b8HvEacYE1eVSLkgzK0DJrJwfg3TIIRAaFfdlUqJdJfuXZK0/MJ1eyRH9kA70fXLSO5ZR3TjMj9cJfwCdqUq7qfLN1SS2LWG1N7jDP3ys1mvGuByyOfWTvpq1KThtx4juWcdWkV0orvMYIbC6UtYvSms4QxeroRbLCMtB6/soMbDNP7XT2K23IRC2ttUJiIEsxfuqcYlmBT6tVIjU6aph+oab8rq5GahhiMoZnjSCnTGKAK9snrS+yhtG3tkbr3qlJCJEon4K905llToldWTb8yk53uABiwoFs6vYRqk5+GWbMbv4VTFD9sscbxCmXKhjD2QIXuwA6MuSeye5SR2r8VsrUUxNIQQqDETJRqi5iu7MVfW0/1nv8JJzXJ/Uo7/dcuofmYHyQfW++J22f7qQj9DL35K/lQ3bsEXNOm4vth6nj8nehKjLom0FtnehhAIY3bhOaEpE2/m8MPH16443VwON5NBOs4EQdMSCUINTTi5zNztsW4ifsF7NUooNKcGpUJRCLdM7v3m2Tb28ODcxxRPokaic+53F25unbS3KD1JeaB3mlcE3CkWvFJIT475A/oouooaC1/nFUuLy96a9kiWYucAI28cIby6kcpHNxHfsQo1bIx1Ow6T2LUaZ+Rher73xoJqCWQ0VpK8f90E8+jC6R4u/YeXKV0anlm6/gLaW5oJQlVQwrMLhSkhHSV0JfQlPb+rwqSVhvQo9V0ivHwVWuyKm4ZQVOIb76HY2YHnzs3g+GZjNjSRC0fnKHAqkRVrJj3u2RblobmvlozqWvRExZwFLrJizeS9RelR6pu98cDNQAkbfkbzAq7zvVMs+FlD2s6EejfFNDDqk5PudJc8nsTLl7EH02Q/O0v3n71C57/9R4rtvePhRcXQqXxyK6GmqptU7HNziG1uRa+OTyj16P/h+5Q6B2YobmLCPuxiQOgaek3ixk+8Ci0eQYtfuXlzMsVpWxHlz5/DmcIRP7ZuM2Zj84K5IQi3rkBLJG/8xGsRglB9A6GGiQbG0nWxR4cpD8xd4EINzejVc8vKNaprMRtbJry/UkrsVIpy76VZH0+goAiNcKiKZLQFTTVRlRCKMvOa0uTOVb4xecAkFvwKzitaFM/3k3xgvf+AItCrE5htdb711l2ItF2cdIHcwXYupnK0/dGvYTRU+CHLsEFsSxuli0OTLJ7uFEZDBcK48lWzBtMUO/pmluwhBGosjDZH55I7hRLSCDVX3viJV6FVxyZcpz2UmTa7tdzbTbm/B6OqZkIavRIKUfPEM/T88HsLIulBS1QQWbkWa7AftzDz0LlQVCp2PzIpacYtFSm0nwZ37hEKNRojsnwVxYsXcFKzK49J7nwAJXxNGyvPI3fqyJwswEJGnHikEVXV0dUwmhpC00wsp8Bo5vyk59c+u4PYhia4Kg8h1FJFuWcEe5ruE3czi0PgzvaOucz7G7tGYwXxXavvWoG7jHQ8iu19FM/1+iskQ/OtzWri/r7+nR7gGEo4NKG1kZMrzrio3O8gsGxyL7UFjhIJEV2/jEE+ntHz9eo45rIalPAVsbL6Rqev9/RcMof3E25uw7hqNSKEwGxqoeH5b9H30gs4mev0lbsNCEWh4t77Kfd1kz97cmYiIASxjVuJrd888XEp8Qp5cmdOzHtMsQ1bKPf3kjm0b8advSOr1xPfdM/ERB4pkY5D5tjBOY3F9RwkEkOPogiNSKQWVdFwpnHAsfpS9J/uptyXxv+FC6qf2IQzi6bDdxMLXuCk61HqGSF39ALxbSsB0BIRkrvXUjzdQ/Zgxx0e4R3Gk5Oy7LySvaDKcbySPSFLU4uHp/RSnAq9Okb1F++9raUENwOhqZitNcTuWU7u8IUbPj+yrpno5rbx65SOS/FsL+W+6Y2eC+fPkjtzguT2+1DNK6FNoapEVqym5dvfYWjvG34TzllkDCohk3DrCsyGZoo9F/0V0zxQY3Fqn3oOJOTPnbqhm3907Qbqv/BV33PzKtxyiczxQ9ijs+vqPuWYwlGqHnwCPI/M0QN45evvEYbbVlL3+S+jxRITvosSSebIZ3POoLSdPMPpc4ykxYQifSmnvgHMHDw/VlR+5Qee+ujMjBoO340seIEDsIczjL59jNjWFYixot/wygbqf+NREJA9MHORU+NhPMu5pT6Es0UYGuEV9XiW47ecmUVo0WyrJbyqAaFf2fQuXRpeUAWnVn9qQtKLXp0gvLyWbDp/3WvVquM0fecpjIaq2zHMm4oQAr0uSe3zu/2yj+uEj8zldVQ+tolQ05WQZqlzkOL5GzjWeB4jH7xFqLaByIrVEzoMCFXFqG+k8avfwup/jNyZkxQunMMeHcYtFZC27XdnCJlo8SRGVTVGXSPhljZCDc2oZhg7PYKdnlsnhUnvRWU1DV/9FrmTR8kc/ozipQuTXD9C9U0kd9xP8p5diGvqzKTnYQ0NMPrJ+zfluy2EQEskqf38c0RWrSN9eB/FC+1413Qf12vqSG7dSXL7LtRYnGs3t91cjqF3XpvXmKR0Z5zILF3PN0FQxLjIWYOZhROuWWAsCoGTZYfc4QuMvH6I6qe3A36YIbK2iWX/85fJ7m9n9K2jFM71TioCV0wdo7GK6LpmYttXEF5RT89fvkHmkzm0u79FKIZGxaObqHpyK8X2PjKftZM7fJ7SxaFpJzjF1Enct4a6rz+EUZdEjP3w7FSe/JELCyqjqnDyEm6+hF7tZ/wJRdD4naew/++fUTo/MHlyUBUSO1bT8E8fwVxe598x+9GYRYXQVGLbVtD6r77KwI8+IHvo/ARBF5pKdNMy6r72ALGty8eTcDzLIf3xafInb5y04Oay9L/yUxq/8i3Ma9LXhVAQukGouZVQYwtVDz8J+HWA0vP85wrwnYvH3GLElX/7mYJzf9PTBz5Gr67FbG5D0TQUI0Ri6w7im7fhFUs42TRuIYfQDPREEjUe9885qcZM4uZzDL37Kl5xfhZ9udPHQEJk5Vq/WFs3iK3bRHTNBrxyCSebwc3n/J6BsQRafKzQfCpDAikZeO0l3CmSfW4lkRV1hBoryZ28hD08sy7zdyuLQuDA33Af/Nkn6DUJ4ttXjrmJC7SKKJVPbKXy8c14ZQcnO5Z5JkCNmaixMIqujv9wpeUsyP0cofgO+dEtbUQ3tYJ8DLdoYfWlsIczuPkyeBIlbKDXJDBbqv39mjH3cokftuj7/pszdrC/XZS6BskeOo9ek0AZK2swl9Ww5t/+NtnD5yme68XNl1EjBkZ9BdGNrRgNFaAIpO2SO9lF4XQ39V9/8E5fyoyQjku5N0W5Z5jEfWuIblrG8nW/jjOap9Q1gJsrI3QVs7kGo6nStyi7HJqUksynZ0l/eGrG7Y/s4UF6fvK31D/ztbGVnP+zvnxMIQSo6vgKT8qx5ky3OOzrOS5D77xK1YOPE12xBhTVXzUqCiKmocYmJw5NCP+N3fh4xQKDb71M4eypeY9Jui6pA5/gFPMkNt877pfp9xqMokYmm1tPKjSXEqRk6K2XyR4/NO8xzRYnU8RcVo1iaP7vX7KgIjYLiUUjcADli0Nc+v9epu5rD1D15FYYEy6/T5yKElYwwsak1109eVz+s+C47LAhGBdgNWYSXt1AeHXDlC/xb74F0vPwLIf+v3uX0b0nbosV1Wzpf+F9Qs1VxLYsB31sQg9pJO5bQ+K+ybVOALJsk9nfQfefv0J4daM/MS+GvThFwc0U6P3emyiG5ncR11T02gR67eTSASGE/510JZnPzjLw4w8odc6ukNlJjdL9D9+javcjVOx+GC0aQ46t5q59z270Hl6ewKXnIacwdZ4pimnipFP0v/Qjaj/3DNG1G1FC5o3HIKW/KPE87GyawVf+kdzp43Mex9UII4RXLDD0xst4xSKJe3b6vpJiBn6nY3OHVy4x+NrPSR/eN2c3lPkgpSS+pZWap7f5Fnue5NL336F0cf57k0uNRSVwAPZAmt7vvUn2YAc1z+wksq7Zd4BQxlpzXPsdHbOhkp5EOi6Fs703bup5m3FLFrmjnYRX1vshOV3zRXs8ZHTNC8bu2DzPQ7oehdPd9L/wPvkTFxdMacC1uJkCXf/+JRp/6zGSe9ajREJ+s8QJoSjGPy83V2L45f0M/OQjvxayP42bKd7StkE3C6EIREjD6k/R9f/8nNqv3EfVU9t8Q2lFmdgzzJN4noubLZLae4KhX+zD6kvN7cSOw8gHb5E5tp/k9vtJbN2JFoshFXVC+HESlwVNSpAenmVR6rlI5sh+8mdPzm0sgBoyEaqGPTJE34v/QHzLdip3P4xRVQua6icaTQr7eWMF7ja5E0cY3vv6TdkHHB+TbiA0HTefZeiNX1Ls6qBy9yOEGpv9pBZlqjGNdQpwHfLtZxh+91XKA313bNVkD2Xp/NNfzd0C7S5i0Qkc+KUDmY9Ok93fjtlaS2xLG5E1TRgNFb51lWn4cfuihT2axepNUezoI3+si3L38C1vlTJrHI/0h6fI7DtLqKWa6MZlhNvqMBoq0KrivluJoYH0nU3cbIFyb4pSRx+Z/e2zqnmT0g+hXV1f5RXKs96zk5aDmyuN92XzCuUbjsHNFLj03ZcZenk/FQ+sJ7qhBb026TcvdT2cTIFyzwj54xdJf3gae+hKirtbtMif6ia6oRk3V/IdPmYyTsfFzZXHr9fNl5G3wcZKqH7I2RnJ0vvXbzP6xhES96/zi95rEyimgVe0sAZS5E9cJPPpWT/B6CbsnTrpNMPvvMrw3tcJt7QRXuZnRGoVVajRmL/3JATStvHsMm4hj50awRoZotx7iVLPRdzc/JvsCl0f3xOUrkPm0D6yxw4RWb6KyKp1hBqa0JOVvvmx5+EU89hDgxQvdpA9eRQnPUehv96YNO1KqNZzyZ06Rv7sKcyWVqKr1hFqWoZRUeWvNKXELRWxRoYodXeSO3UMa2hwylZFtxs1bhJeVkPp0jCe7eJZzoK9ub2TiIUQrhNC3PlBBATMFVUhsXMVK/7kG+MPFS8M0PG//QDnLii+FZpG7dNfoXLXAxMez58/x+CrL1K+ExZWqkrl7keo+/xzEx4u9Vxk8M2X5136cCfR4ia1z+4gvqWV7r9+l9jmZaQ+Pku5e5Y9HRcBUsp57UksyhVcQMBiQCy2tM+ARYFRl6TQ3o9QlTFjcunv2wvmnlEphB+NUcYO4soZmzEsZAKBCwgICFhElPtSxDa2EGqsJH5PG2rM9MuJ5iJuikCNh9FrkoSX16EmIkjHxR5MU+oaxEnlF3X/zbte4ERIR6+Iju8l3Q6k6ycVuIG9TkBAwCxx82VG3jmBNZhBCRuMvnsCay6hcFXBXF5P3T95gNiOVX59pOv5OTaqgpspMvr2EUZfO4g9dOd9TefCXS9w0c2tNP/uU5gtNbftnPZQhoEff8jQLz67becMCAhYIoxl6TqZIuTLCMNPnJlVSFFAqLmalv/+WbREhMKJixTP9+Pmir4LTk2C8OpGqj63Db0qTt/fvImbWRgtmGbDXS9wAQEBAYsJLRmh5nNbAYlbtDEf2UD/i/soX5p5kokwNKq/tBMtGaHve2+Qev/EpAxevTZBzfN7iG1bQWLnWkbfOnyTr+TWc9cLnCzZWAOZSR16byVOKr/g3EYCAgIWB3oyQvHiEKWuIZxMkerPbUGNhFBjJu4MS358G7mV5I93kdo7dRG9PZghtfcYkbVNmKsbIRC4xUepa5D+v38XJTTzBoPzRdouVv/Nr/EJCAhY+ljDOeLblmNUx/FKNkZ1nOiqeozqONnDnb7I3QAhBGrMvOE85JUs3FwRNbq4Gg5fZskIXCwuaFymo6rQ1+2QGplZ0aObLVI4dWdazQdMTTypsHyVjm1Jzp+zKZeCMsmAgMtIxyV/qhst6tueXe5U4bnejG0IpZQ42SJ6XQVCV6c2vxgTQSVq4vbePDeZ28mSEbgtO0z++R9VEo4Kvv/dNL/40fydGAJuP6oKex41+Vf/upqhAY///Q8HOXlkZqbDNwshQDcErivn0zg6IOCW4BUt8qd65nUM6Xjkj1wgsWsN8Z1rKJ7r8R2CXA+EQAlp6DVJErvWooZDFE4vzkXAkhG4eFKhulbBMBUqqhZet4CAmSEUaGjWCIUVIlFJZc3tK9+4TGW1yo49Jh1nbDrbLWbShDogYDEhLYfR1w8SWdtE0+8+TfbAOUqdg3jFMkJV0KriRNa1EGqpIfPRKXKHFmdj6SUjcOdOWrz0wxxmWHD4s6C+bLHiOvDx3iJtq3RGhjxOHLr9n+U9u0L8d/9rJS/8ZYbeSzaOc4Owj5TYo3nSH1+xf7IG0njWwmmqGxAwASkpnu+n76/fpPrZ+4hsbCV+31oUTfM7JpQs7OEsqbePMPL6QdzM4uwYvmQEruOMzZ//uyBxY7EjJZw5bvOv/+Wda/2xaVuIaGwWUQBPUjzTw4X/40e3blABATcb1/MbK18YILKxlVBzFWrERLou9kiOYnsvpY6+hWdOPwuWjMAFBNwMzLBg7UYDMxL4SM4U6UlKPV1kjpoTHi8P9uMW79Cdv5RYQ/1kjh6Y8LA9OoKTW5yuHLcE6ZctZT6ce1ukhcyi6yYQjghWrTdYtnx6bb5wzubMcYvrdUWJJQT1jRrVtSqxhIJuCKQHxaJHatjj4nmbdMqb1G5p+30hGlo0SkXJu68V8K5zjnhCYeM2g8pqlb5uh6P7yxPGZIYFtQ0qtfUa8aRCyPQn1XJJkkm5dHc5jAy6N9wDqqlXaW7VqKhSMU2BEGDbknxOMjrs0t9z46xSTYe6Bo2GZo1EhYJhCCRglST5nMfIkMtAr0MuO/ENicYE67eEqKxWOHqgTH+PSzgqaGnVqGvUiMQUpCcpFiRD/S4dZy3sa3JGwlHBI09FuLYUsVySfPZhiUxq+rFX1Shs3m5i25JTR8sUcpKGFo2mZRqxuOJ3yE55XOp0GOhzcKaIGja2aNTUqySSCs1tGt/+3SQNTRqvvpjn4MdFyuWJ13z+rM2po7c38SUg4KaiCMy2Or99mHWDCUaAYhqoURPpebjZ4m1b1d113QTiCYUnvhjhS/8khqYLNA00TaBqV7oEv/BXGS6cG6VYmFo31240uO8Rk83bQrSt1qmp04hEBK4LmbRLd6fDgY9LvPZSns52e7xprxCw57Ewv/n7SUaHPdpP23R1TL/P0rZa53/6kyrqmzR++eMcxw9eEbiGZo09j5ls3Wmycq1OfaNGNO7P8PmsR+8lh6MHyrz9Sp6TRyxKxcnXoiiwdWeIh5+KcM9Ok+ZWjVhCQVGgXJSMDLlcvOBw/FCZv/uL9JTHEAIqqhR2PhBm54Mm67eEqG9SiUQEUkIh5x/nzAmLl3+SY98HEwvUq+s0vvmdBPfuNvk3fzSE45R58PEwDz8VYe1Gg8pqBdeF9KjHwU9KfPf/GmV4cOKPI5FU+IM/riQaUzBCAl33P8fhQZc//J1+MqnpxaRtlcEf/HElxYLHf/mPGSxL8sSXomzeblBdqyIl9Pc4HPy0xJu/KHD8UHnC90JR4emvRNnzWJiGJo2qGhVt7PxPPx/l6eejk8759/8xzZnj1p1o5hwQcFNQQjp133iY/r97h3LP8HgdsFeyJpo2qwqhpiriO1ZjNFQibZdiey/5Y52Lwp9y0QlcLufx8d4iqREPIyQwQmAYgtoGjU3bQjQ03/iSNt8b4td+I07IVOjvdjjcWaJUlOgG1NZrrNtssHl7iNoGlT/9P0dJj/ozmZTw1q8KfON3EkSigoefCvNf/mJqgTPDgpVrdJYt1+nucji8r4R91VOXrdB47utxWpZr9He7nBxbfSgKVNWotK3SWbvRX6n+xb9PcfqYNWk1uXqDwX/zP1awbZfJQK/D6WMW+Zy/6oxEBdV1KhvvMaipU/nBX2aYym68slrhy9+I8+VvxqlvVBkddulqt8lnPYQiiMb84yxfrRNLTL8vzVPiAgAAEpZJREFUFQ4L6ho1vvhVjWe+FkN60Nlh035aYkYU6upVjLHV5aTPNOPxwl9miMUVonGFNRsM7r3fnPzE61BZo/Lks1GSFQqJpELHGX+VlUgqLF+t89zX4yxbrvP976Y5+GnpSvq/9M9//qzN+bM2yQqFnQ+YxJMqRw+U6Gy3J62gjx0qB42UAxY1QlWJbV9F6Xw/TjqPlvRv5Kz+FIUz3Vg9vu2XVhGl8nPbSD60EWc0h2IaxO9dxUhFlJHXDi74TgOLTuAKOcmn75X49L2JK4n1Wwz+2b+omJHAHT1Q4pc/VigVJe2nLHq7HfJZScgUtK7Uee7rMR54PMxTz0V5+Sd5Du0rjYciO87YnD5usWlbiIeejPCj72exylMJh8q9e0yEAj0XbY4emLgKuXje5pWf5khWqpw9YdHd5YdEVdUPmT3+xShPfCnCzgfCfPROka4Om0J+4nk+90yUDVtCjI64vPBXGQ5+UiI96gtcokKhuVVjxVqD1LBLPjt5uRGOCB58IsI3fidBOOJnn+59vcCZ4xbpERehQEWVH/60bThxePovs6IKHv5cBF2HYwctPnmvwKXzDpYlicUVlq3UGep3yU0xjnxO8vf/yb8bVDV49muxWQtcVY3Ktl0hjh8u8+O/yXLmhEW56FFVq/LA42G+8NUY23ebXOp06Lno/wHwPPjp32URY9q9ZoPB8jU68aTK268U+PkPcxTyE8fseQQCF7DoUQyNys9vRwnpvlAJAQIyn5xh6GcfYw+mMWqTxLYsp3C6m9HXD6IlIyQf2Ux81xoKp7spnLx4py/juiw6gbseM51zOk7bdHVksG05aQ+tu8shn/PYfG+IugaNDVsNjh0sYY09z7Elr7+UZ8u9JitW66xer3Pi8ETxEgLqGlW27TTJZT1OHbMY6J24DBjodXnxhRyeJyftC/V1u6RGPJav0rnvYY3lq3USFQqF/MTBtizXMEKC08cd3vxlgZGhK/8/NODSccbmvTeKaNN8ynWNKl/+Zox4UuHUUYvv/WmKzz4sXTN523z24Y3eUZ/N2w1+/qMcP/hPGbq7Jl7vkf0zv9Obi3boumCo3+WlF3K8+1ph/CADff4eZDyh8Py34tz3kMlH7xTpveSMX6fnAWMa5rpXBiA9cJ2g2DtgiSL89jjDr3yG3Z9GjLXPiW5qxeodYfgX+1DCBkrYoHC8i9zBDlAVEArVz+3CXF634AXurqyIdl0/gWG6BJEzx/w9Lymhuk5FUa7E1TwXPnq3SHrUxQwLHn06Mun1kahg3aYQdU0qA70u+z+6VjT8SdUqTxa3y1zqtBkZcpESkpUqodDk2N7IkIvjSOqbVLbuCBGeJvNvqiQVXYfWlTrrN4fIpj0+fLvAZ1OMczb0XnLY+1phfHV0O5FS0tlhc+Sz0iSFHB32OHqgTF+3Q32TxrLlGmY4yJIMuLuRtkvm41MMvPAeo28cYuTVAwz+9ENK5/uJbmwF8DuFKwK3OHaD6nrYAym8QhmtInYHRz8zltQKbjYIBWrrVZpbdWrqVKIxgWH6CQ6qJoiP7TcZ+uSJcKDX5cDHJR59OsKuh8L87Z9lJoTeKqpUdj1k4jpw8YLN6WNTJ0kIAclKheY2nboGlXjCz6TUDT95Zvlqf+NX00Aok8fx/psFdj5g0rpS5zd/L8m6zQYnjpQ5e8Kiv8e9rliFwgqr1hnohiA96nLwkxJynkkT7adtBnqvf95bhW37SSnTZYv2XnIY7HdZudagvsnPWi0WFm99T0DA/JB4ZZty5yC4V34zXsnC6k8R3bjMf0AI/4bxqg4FXtlG2i5KaOHLx8If4S2guk7lvodMtu3yxaGmTiUcFYBAehJP+tZfUyVEADiO5I2f53n8C1GaWjQ2bQ/xyV6/GaCi+hmSm7eHSI+6HNtfJpuePOnGEgr37Ayx60GTFWsN6hpUYnEFoVwZQ+KqMUw1lIOflHnhrzI89VyUTdtDrN1kcKHd5swJi9PHyhz5rDxttp+uQ22Db4NVKkr6uuc/2aeGXYqFO5NaaJXleILNVOQy3vheWjyhYJrBCi7g7kVKiZPKE17TRPrDk3hFC4RAS0QIr6hHjYeJbGzFaKhEqArCuEoqLtfzzKAtz53mrhO4yhqF574e45mvxaipU2k/ZbHvgxKD/f7em10Gx5X83r+spLp2ah9Ez4WjB8t0d9nU1Ks8+vnwuMBFowqbt4eoqFI5daTMvg8n932LxgQPPRnm67+dYMVqnb4ehxNHLPouOWQzHlZJ4jiSZ389xtad0ydblEuSl3+S50K7zb27TbbcG2LD1hCr10d56Ikwxw+X+fjdEq+9mJu0shGC8bCn60K5OH9hsp3pw763GunJ66btuy7jY1M1gaIGAhdw9yJtl+xnZ6l4ZDPSdrAG/D24UGst5op6rP4U9d98GBHSQRGEWmpQwgae5WDUJVHjYZzswrfvuusEbtcDfkZdU4vGG7/I86uf5Tl7wmJ0xB1PJtB0+M3fS04rcACZlMf7bxX4+m8n2LrDpLpWZXjQJVmpsPuRMOWSR/uYWe+1rFhr8Oyvx1i32eDwvjI/+0GWYwfLDPW74xmZqgrbd5ts3Xn967HKkkOflDl1xGLlWp3VGww2bAlx7/0mex4Js35TiMoqhf/8/6YmJEtIyXjBtaKCYSqMZ1rMlTt4Q6eqAv0632a/XtL/t2NLXHfh330GBNwqpO0y+tYR9OoEyQc3+is0T+LmS2Q+Pk3uWCexbStQQgbFsz2EV9ZT9+1HkUWLyPoWpONSujBwpy/jhtxVAifGCqMbW1T6ex1e/VmeT98rTnI8icUV1Buk39iW5N1XC/zaP01QXady7/0mb72Sp3GZxrpNBqPDHgc+LmJdkzwoBLSt1NmwNURm1OODtwq886vCpFIDM+Lvxc2UUlFy4rDFicMWn7xb5KN3Qnzl2zH2PBbm2V+P8c6rhQl7gbYNg/2+4pmmoKHJd1tZrBgh4Re5q0y5iownFSJj/pKZlEdpGhOAgIC7AikpXxqi/4W9RNY0oSUjSE9iD2UonuvFHslSPNc7vs9W8fgWkg9sRI2EcFJ5Rt8+QvFs7x2+iBtzVwmcpvn1YaGQwmCfxfCQO6Wd17pNoXHbrOnwPDg/Zgm2ZoPOnsfCfLy3yObtIcJRwblTvhvKtSgqROOCaExhqN8vH5iqjq6lTaeyem6tYvp7Xfp7C2QyHnseCxNPKmzYakwQuHLJo+O075RfUaWybbfJoc/Kd3QVNh9UTVBdp1Fbr9LfM/lDbVqmU9eg4TiS/h4/FDwVnivH9/F0Y+rC9ICAJYEnsbqHsbqH4XIS21X7as6I30gVIRh97RCl9j6UMYErne/HzRXvwKBnx11VJnA5Nd91pS90U4hYslLhi78Wva5rx2WKeck7vyqgG4K1mwxWrNbZsSdMISc5ecRioHfyRCs9f/Xn2JJwRBm357qaSEzwwONhlq/Spz23qvrlCNf6N15NIXdlEr82Q9K28BNSjlvEkwoPPhFmx3WKq4VgQU/2QsDyVTr37jYnvSfVtSpbdoSob1TpvejQdWH6LuG5rDceym1Zrs1qFR0QsGjx5PRJI9Jf2WU+OUPq7aPkDnbgpPK3d3xz5K5awbkOdJ13SI96NDRrPP6FCIWcR89FB90QrFhj8MSXIty7x8TzwLtBlpBlST59r8hv/X6CymqVB58I++HJEZdP3itOmfTgeTDY79JzyaGhSeP+R8J0ttucO2kjgeZWjQceD/PkM1HMiJi2F1k0pvBbv5+kkPc4d9qm96JDJuXieb55cdsqnWe+5tep5HOSY1P0VRvodXjxhSx/8L9Usnq9we/8DxWs3VTg9HGLzKiLqgriSYWGZt+j8djB8pSr0pvN1RURM5UX15HU1Kl8+ZtxNF1w7GCZfM6jpk7l4aciPPhEGM0QfPJekXMnJtueXWZ0yKWvx2H5ap37Hgpz6qjF/o9K5LMeuiGIxRVSIy6D/UGJQUDAQueuEjiAD94qsHVHiN0Ph/n88zHWbjJIj3p+iKtWpWmZxqsv5mhb6a/Grof0oL/XN/Ld82iEJ5+JEo4KThxxOHZweueOM8ctPnizwFe+HWfHHpOaeo2hsf2wZKVKY4vGsYNlOk5b7Hxw6jHohuD+x8LU1KkMDbhkRj2KRT9N3ggJqqpVWlfp5DKSH/znNJ3tkyvKC3nJ+28UqapR+epvxLlnV4jWlRoDfS6lgu+LGTIFiaSC4/hG1Ac+nsWbPQOqanyHkWjcT90PR5Tx+r9YXPC7/6KCoQGXckmOlTM4vPGL/Lg/6GWyGY9zp2xCpuC/+udJ+rodymXfJqylTSdZqbDv/RKv/zx/XXGyLPjVP+ZYuVansUXjW99J8PgXIpTLElUVhEzBiz/I8tpL+cCuKyBggXPXCVznOZu//fM0A70OO/aYbNoWQlEgl5VcOGvxN/8hzbuv5XnsC1G27LixH2Kx4IcpH3kqQlOrRjbtcfjT0pS1b5cZ6nd56R9yFPKSB58Is2K1zrpNOsWC5FKnwys/zfHOrwq0rtJZtd6Y8hi5jMeLL2TZ/XCY1hU6azcbmGF/z6hc8lvTvPOrPB+8WeTjvcVpHVNGhlx+9oMsXR02ux8Os+GeEK3Ldcywv3rMZT0Gel0O7ytxapqC9flQUaXylW/HicWV8e4Q6lgKf8hU2PNYGNf1aw8d2+/cvu/94iSB81w4fqjM0QMlnvhilHt2hqiuV/Fcv8j79V/keOtlP9Hmem2UwO8orumCzz0bZd1mg227TZBQLHgM9ru4N+rwHRAQsCC46wTOceDYgTIDvS6vvZQnGldQhL83lxr1uHTBJp+XvPVynq4O208Csaaf0GxL8tE7Rf749wYB/zgXzl1fCDwPOtttfvK3GT56p0iiQkFVwbH9VVLvJb9/20Cf3w+uVPTov8bLslyWvPqzPAc+KpGoUAiHFVTdD+m5rr86Gx70TYWnEzfwywVGhzz2vl7g5BGL6jq/4FzTxvYsLUkh5zE86DIyNFm0B/sc/vq7aX7xwxyXOn3D6Nkw0Ovy7/5keFzUbkQ+6zE0MFmhVM1PnPn0vRIXztrUNapEogpyrFtAb7fD8KA7I1/JfNa/aTl70qK6VvVtvaRfXlDIS7rO28HqLSBgEbCkBG58ipTXd3t3x+7qey9NP9v197j099w4S0hKSI14vPvq7IoePQ9GhjxGhqYPZY4Oe+z/aPo9r0zKu24z0NlgW0xw2Z8pxYK8bjj2RuSyHu+/Mf9sLCH8MhCr7K+CL3XOr+ShXJJ0nLbpOH2du4OAgIAFzZLJojQjgmhcGStglth2cIt9txHkOwYEBFzNkhA4IyRYvsqgdYVOJu3OOBQVEBAQELB0WTQhSkWBpmUa9+wy6e60SY16OLYkWaly7/0hnn4+RrJC4dC+EiePLOwuswEBAQEBt55FJXBtq3T+2z+soFT0Q5BSgqYJEhUKyQqFrvM2v/xx/pZk+wUEBAQELC4WjcB5EtIpj84Om5Y2jZp632XCLkt6ux3e/GWed18rcPJweVqXioCAgICAuwchF0C+sxBiRoMwQoKqGgUjJFBV36bKG7O+yuc8shnvuinxAUsPMyyoa1RRVb9x61SlDAEBM0EIFVVoGFqUsp1DUVQct4yqaHjSpSG+gXSpF9srYTl5QmqUsptHFRoIBdcLIkc3GynlvHLHFpXABQQEBNwqwnqSmugqLCfHcKGT2thqhvMXSJgNFKwR6uNrKVijxEI1dKUOUhdbw2CundroSgbz7Vjuwu+PttiYr8AtmhBlQEBAwK1ECIWykyNT6sfxLDTFQAiBpugoQgUE6VIvmhIipEUZKXRRF1uNqcWx3IXvrH83siTKBAICAgJuBp508KQNSIp2iob4eqoibSiKhpQetbHVxM06LCdP0U4T1pNkrSEWbZ+pJU4QogwICAgABAIhFDzpARJFqChCAwSuZ6EIFYHfN8rxyoBgdc1DnB/5GNcLNv9vBcEeXEBAQMAdoCq8DMsrkisP3emhLFkCgQsICAi4AwjEWGAymL5uFUGSSUBAQMAdQAbCtuAJkkwCAgICApYkCyJEGRAQEBAQcLMJVnABAQEBAUuSQOACAgICApYkgcAFBAQEBCxJAoELCAgICFiSBAIXEBAQELAkCQQuICAgIGBJEghcQEBAQMCSJBC4gICAgIAlSSBwAQEBAQFLkkDgAgICAgKWJIHABQQEBAQsSQKBCwgICAhYkgQCFxAQEBCwJAkELiAgICBgSRIIXEBAQEDAkiQQuICAgICAJUkgcAEBAQEBS5JA4AICAgICliSBwAUEBAQELEkCgQsICAgIWJIEAhcQEBAQsCQJBC4gICAgYEkSCFxAQEBAwJLk/wfUpEP4VLCPNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
